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In this lecture Gabriel Moldovan goes into more depth on the AIFS, from Architecture to its use cases. Gabriel shortly goes into the development of AIFS single and discusses the accuracy compared t...
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...d mean for people, staff, and operations on the ground.
#DataForPeace #ConflictForecasting #EarlyWarning #AI
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Forecasting where violence may move next means understanding how conflict takes shape on the ground. I...
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Wildfire prevention is one of the most effective and economical risk mitigation strategies. Human-started wildfires account for over 60% of all recorded wildfires across the western United States a...
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This is a recording of a live session from the DHIS2 workshop on spatiotemporal modelling of climate-sensitive disease that took place in Kigali, Rwanda in February 2026. The video details methods ...
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...ansitioning from standard machine learning regression to multi-step time series forecasting, featuring a hands-on session using Python-based models to predict disease cases like mala...
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...come remarks by João Sousa, European Central Bank, on the first day of the 2026 Forecasting Techniques conference.
This biennial European Central Bank conference provides a forum fo...
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Artificial intelligence in the analysis of economic narratives, forecasting, and risk assessment.
This biennial conference provides a forum for new theoretical and a...
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Artificial intelligence in the analysis of economic narratives, forecasting, and risk assessment.
This biennial conference provides a forum for new theoretical and a...
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Lecture 23: Integration I: Automated Conditional Data Extraction
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This advanced lecture in the "Climate Modeling & System Analysis" module teaches participants to engineer automated pipelines that transition from manual handling to code-driven workflows. It i...
Lecture 16: Static Visualization I: Climate Time Series Analysis with Matplotlib & Seaborn
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Teaches how to decompose time series into trends and anomalies and automate routine products like Monthly Climate Bulletins.
Lecture 14: Statistical Calculation I: Algorithms for Climatology
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Introduces the specific statistical algorithms required for advanced climatological calculations
Lecture 12: Missing Data Control: Spatio-temporal Interpolation
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Teaches advanced methods for diagnosing gaps (MCAR, MAR, MNAR) and implementing interpolation techniques like cubic spline and imputeTS in R, specifically evaluated for archipelagic geography
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📈 Conflict Forecast
A forecasting tool that predicts internal displacement, access disruptions and anticipates food insecuri...
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If you felt like this winter was 'off', the data confirms you’re right. In this edition of TAKE 3, Vibhu Batheja Research Associate, TERI explains why we have witnessed one of India’s most erratic ...
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The world’s first quantum-enabled algorithmic trading has already increased forecasting accuracy by 34%, while breakthroughs across other industries unlock potential once thought...
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...uggested that, on average, their accuracy is comparable to chance. Crowdsourced forecasting offers an innovative approach to government foresight. Crowdsourced forecasting is the pra...
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